Name
Title | ||
|---|---|---|
Automated interpretation of 3D laserscanned point clouds for plant organ segmentation |
Abstract | ||
|---|---|---|
BackgroundPlant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1186/s12859-015-0665-2 | BMC Bioinformatics |
Keywords | Field | DocType |
Automatic segmentation, Clustering, 3D-laserscanning, High-throughput, Plant phenotyping | Plant phenotyping,Data mining,Data-driven,Plant Structures,Segmentation,Computer science,Plant growth,Bioinformatics,Cluster analysis,Point cloud | Journal |
Volume | Issue | ISSN |
16 | 1 | 1471-2105 |
Citations | PageRank | References |
2 | 0.44 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mirwaes Wahabzada | 1 | 103 | 7.41 |
| Stefan Paulus | 2 | 108 | 9.26 |
| Kristian Kersting | 3 | 1932 | 154.03 |
| Anne-Katrin Mahlein | 4 | 79 | 8.59 |